library(tidyverse)
library(plotly)
library(sf)
library(mapview)
library(tigris)
library(censusapi)
library(leaflet)
library(lehdr)
library(usmap)
library(lmtest)
library(pracma)
library(lmtest)
library(forecast)
library(vars)
library(rvest)
library(RSelenium)
library(seleniumPipes)
library(dLagM)
library(jsonlite)
library(rgdal)
options(
tigris_class = "sf",
tigris_use_cache = TRUE
)
Sys.setenv(CENSUS_KEY="10dcd73d7c043e91bac9fb8d3989cbff54b08790")
Get the cumulative case data, first for SCC.
# remDr <- remoteDriver(
# remoteServerAddr = "192.168.86.25",
# port = 4445L
# )
# remDr$open()
#
# remDr$navigate("https://app.powerbigov.us/view?r=eyJrIjoiZTg2MTlhMWQtZWE5OC00ZDI3LWE4NjAtMTU3YWYwZDRlOTNmIiwidCI6IjBhYzMyMDJmLWMzZTktNGY1Ni04MzBkLTAxN2QwOWQxNmIzZiJ9")
#
# webElem <- remDr$findElements(using = "class", value = "column")
#
# cases <-
# 1:length(webElem) %>%
# map(function(x){
# webElem[[x]]$getElementAttribute("aria-label") %>% as.character()
# }) %>%
# unlist() %>%
# as.data.frame()
#
# scc_cumulative_cases <-
# cases %>%
# rename(text = ".") %>%
# filter(grepl("Total_cases",text)) %>%
# separate(text, c("date","cases"), sep = "\\.") %>%
# mutate(
# date =
# substr(date,6,nchar(.)) %>%
# as.Date("%A, %B %d, %Y"),
# cases =
# substr(cases,13,nchar(.)) %>%
# as.numeric()
# )
#
# saveRDS(scc_cumulative_cases, file = "/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/scc_cumulative_cases.rds")
scc_cumulative_cases <- readRDS("/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/scc_cumulative_cases.rds")
Also for SMC.
# remDr$navigate("https://app.powerbigov.us/view?r=eyJrIjoiMWI5NmE5M2ItOTUwMC00NGNmLWEzY2UtOTQyODA1YjQ1NWNlIiwidCI6IjBkZmFmNjM1LWEwNGQtNDhjYy1hN2UzLTZkYTFhZjA4ODNmOSJ9")
#
# webElem <- remDr$findElements(using = "class", value = "column")
#
# tests <-
# 1:length(webElem) %>%
# map(function(x){
# webElem[[x]]$getElementAttribute("aria-label") %>% as.character()
# }) %>%
# unlist() %>%
# as.data.frame()
#
# tests_clean <-
# tests %>%
# rename(text = ".") %>%
# separate(text, c("date","test_text"), sep = "\\.") %>%
# separate(test_text, c(NA,"type",NA,"tests")) %>%
# mutate(
# date =
# substr(date,23,nchar(.)) %>%
# as.Date("%A, %B %d, %Y"),
# tests =
# tests %>%
# as.numeric()
# ) %>%
# spread(
# key = type,
# value = tests
# ) %>%
# mutate(
# total = Negative + Pending + Positive,
# perc_positive = Positive/total,
# perc_positive_movavg = movavg(perc_positive, 7, type = "s")
# )
#
# smc_cumulative_cases <- tests_clean %>%
# mutate(cumulative_cases = cumsum(Positive), cumulative_negative = cumsum(Negative), cumulative_total = cumulative_cases+cumulative_negative, perc_positive_cumulative = cumulative_cases*100 / cumulative_total, perc_positive_cumulative_mov = movavg(perc_positive_cumulative, 7, type = "s"))
#
# saveRDS(smc_cumulative_cases, file = "/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/smc_cumulative_cases.rds")
smc_cumulative_cases <- readRDS("/Users/simonespeizer/Documents/2020 Spring Quarter/CEE 218Z/covid19/Simone_Speizer/smc_cumulative_cases.rds")
Get social distancing data.
scc_blockgroups <-
block_groups("CA","Santa Clara", cb=F, progress_bar=F) %>%
st_transform('+proj=longlat +datum=WGS84')
smc_blockgroups <-
block_groups("CA","San Mateo", cb=F, progress_bar=F) %>%
st_transform('+proj=longlat +datum=WGS84')
bay_sd <- readRDS("/Users/simonespeizer/pCloud Drive/Shared/SFBI/Restricted Data Library/Safegraph/covid19analysis/bay_socialdistancing_v2.rds") %>%
mutate(date = date_range_start %>% substr(1,10) %>% as.Date())
# obtaining weekends
weekends <- bay_sd %>%
filter(!duplicated(date)) %>%
arrange(date) %>%
mutate(weekend = ifelse((date %>% as.numeric()) %% 7 %in% c(2,3), T, F)) %>%
dplyr::select(date,weekend)
bay_sd <- bay_sd %>% left_join(weekends)
SCC data processing.
scc_cases_sd_daily <- bay_sd %>%
filter(origin_census_block_group %in% scc_blockgroups$GEOID) %>%
group_by(date) %>%
summarize(total_at_home = sum(completely_home_device_count), total_devices = sum(device_count)) %>%
mutate(
percent_at_home = total_at_home*100/total_devices,
percent_leaving_home = (100 - percent_at_home),
) %>%
left_join(
scc_cumulative_cases
) %>%
filter(date >= min(scc_cumulative_cases$date))
# get the baseline percent of people at home
pre_case_growth_at_home_scc <- bay_sd %>%
filter(date < min(scc_cumulative_cases$date)) %>%
filter(origin_census_block_group %in% scc_blockgroups$GEOID) %>%
summarize(total_at_home = sum(completely_home_device_count), total_devices = sum(device_count)) %>%
mutate(percent_at_home = total_at_home*100/total_devices, percent_leaving_home = (100 - percent_at_home))
# include change in percent leaving home
scc_cases_sd_daily <- scc_cases_sd_daily %>%
mutate(leaving_home_dif = percent_leaving_home - pre_case_growth_at_home_scc$percent_leaving_home[1],
log_cases = log(cases))
# compute number of differences for stationarity
ndiffs(scc_cases_sd_daily$cases)
## [1] 2
ndiffs(scc_cases_sd_daily$log_cases[-1])
## [1] 2
ndiffs(scc_cases_sd_daily$leaving_home_dif)
## [1] 1
scc_test_big <- scc_cases_sd_daily %>%
mutate(
dlog_cases = c(NA,diff(log_cases)),
d2log_cases = c(NA,NA,diff(log_cases, differences = 2)),
dcases = c(NA,diff(cases)),
d2cases = c(NA,NA,diff(dcases, differences = 2)),
dleaving = c(NA,diff(leaving_home_dif)),
d2leaving = c(NA,NA,diff(leaving_home_dif, differences = 2)),
cases_mov = movavg(cases, 7, type = "s"),
log_cases_mov = movavg(log_cases, 7, type = "s"),
dlog_cases_mov = c(NA,diff(log_cases_mov)),
d2log_cases_mov = c(NA,NA,diff(log_cases_mov, differences = 2)),
dcases_mov = c(NA,diff(cases_mov)),
d2cases_mov = c(NA,diff(dcases_mov)),
leaving_mov = movavg(leaving_home_dif, 7, type = "s"),
dleaving_mov = c(NA,diff(leaving_mov)),
d2leaving_mov = c(NA,diff(dleaving_mov)),
leaving4 = c(rep(NA,4), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-4)]),
dleaving4 = c(NA,diff(leaving4)),
d2leaving4 = c(NA,NA,diff(leaving4, differences = 2)),
leaving3 = c(rep(NA,3), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-3)]),
leaving3_mov = movavg(leaving3, 7, type = "s"),
dleaving3_mov = c(NA,diff(leaving3_mov)),
d2leaving3_mov = c(NA,NA,diff(leaving3_mov, differences = 2)),
leaving4_mov = movavg(leaving4, 7, type = "s"),
dleaving4_mov = c(NA,diff(leaving4_mov)),
d2leaving4_mov = c(NA,NA,diff(leaving4_mov, differences = 2)),
leaving5 = c(rep(NA,5), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-5)]),
leaving5_mov = movavg(leaving5, 7, type = "s"),
dleaving5_mov = c(NA,diff(leaving5_mov)),
d2leaving5_mov = c(NA,NA,diff(leaving5_mov, differences = 2)),
leaving6 = c(rep(NA,6), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-6)]),
leaving6_mov = movavg(leaving6, 7, type = "s"),
dleaving6_mov = c(NA,diff(leaving6_mov)),
d2leaving6_mov = c(NA,NA,diff(leaving6_mov, differences = 2)),
leaving7 = c(rep(NA,7), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-7)]),
leaving7_mov = movavg(leaving7, 7, type = "s"),
dleaving7_mov = c(NA,diff(leaving7_mov)),
d2leaving7_mov = c(NA,NA,diff(leaving7_mov, differences = 2)),
leaving8 = c(rep(NA,8), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-8)]),
leaving8_mov = movavg(leaving8, 7, type = "s"),
dleaving8_mov = c(NA,diff(leaving8_mov)),
d2leaving8_mov = c(NA,NA,diff(leaving8_mov, differences = 2)),
leaving9 = c(rep(NA,9), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-9)]),
leaving9_mov = movavg(leaving9, 7, type = "s"),
dleaving9_mov = c(NA,diff(leaving9_mov)),
d2leaving9_mov = c(NA,NA,diff(leaving9_mov, differences = 2)),
leaving10 = c(rep(NA,10), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-10)]),
leaving10_mov = movavg(leaving10, 7, type = "s"),
dleaving10_mov = c(NA,diff(leaving10_mov)),
d2leaving10_mov = c(NA,NA,diff(leaving10_mov, differences = 2)),
leaving11 = c(rep(NA,11), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-11)]),
leaving11_mov = movavg(leaving11, 7, type = "s"),
dleaving11_mov = c(NA,diff(leaving11_mov)),
d2leaving11_mov = c(NA,NA,diff(leaving11_mov, differences = 2)),
leaving12 = c(rep(NA,12), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-12)]),
leaving12_mov = movavg(leaving12, 7, type = "s"),
dleaving12_mov = c(NA,diff(leaving12_mov)),
d2leaving12_mov = c(NA,NA,diff(leaving12_mov, differences = 2)),
leaving13 = c(rep(NA,13), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-13)]),
leaving13_mov = movavg(leaving13, 7, type = "s"),
dleaving13_mov = c(NA,diff(leaving13_mov)),
d2leaving13_mov = c(NA,NA,diff(leaving13_mov, differences = 2)),
leaving14 = c(rep(NA,14), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-14)]),
leaving14_mov = movavg(leaving14, 7, type = "s"),
dleaving14_mov = c(NA,diff(leaving14_mov)),
d2leaving14_mov = c(NA,NA,diff(leaving14_mov, differences = 2)),
leaving18 = c(rep(NA,18), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-18)]),
leaving18_mov = movavg(leaving14, 7, type = "s"),
dleaving18_mov = c(NA,diff(leaving18_mov)),
d2leaving18_mov = c(NA,NA,diff(leaving18_mov, differences = 2)),
leaving21 = c(rep(NA,21), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-21)]),
leaving21_mov = movavg(leaving21, 7, type = "s"),
dleaving21_mov = c(NA,diff(leaving21_mov)),
d2leaving21_mov = c(NA,NA,diff(leaving21_mov, differences = 2)),
leaving28 = c(rep(NA,28), scc_cases_sd_daily$leaving_home_dif[1:(nrow(scc_cases_sd_daily)-28)]),
leaving28_mov = movavg(leaving28, 7, type = "s"),
dleaving28_mov = c(NA,diff(leaving28_mov)),
d2leaving28_mov = c(NA,NA,diff(leaving28_mov, differences = 2)),
past_cases = c(NA, scc_cases_sd_daily$cases[1:(nrow(scc_cases_sd_daily)-1)]),
cases_growth_daily = (dcases / past_cases)*100,
cases_growth_daily_mov = movavg(cases_growth_daily, 7, type = "s")
) %>%
filter(date >= "2020-03-01")
ndiffs(scc_test_big$cases_growth_daily)
## [1] 1
scc_test_big_pre415 <- scc_test_big %>% filter(date <= "2020-04-15")
Plots testing
# raw, no shifts
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - Growth Rate, No Lag")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - Cases, No Lag")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = d2cases_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Change in change in cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - Change in Cases, No Lag")
# log cases
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = log_cases_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - log(cases), No Lag")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = dlog_cases_mov*100-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~(.+30)/100, name = "Change in log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in log of cases, no lag")
# raw, no shifts, pre april 15
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - Case Growth Rate, no Lag, pre 4/15")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving_mov, color="Leaving home")) +
geom_line(aes(y = dlog_cases_mov*100-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~./100+30, name = "Change in log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - Change in Log of Cases, no Lag, pre 4/15")
14 day lag
# 14 day shift
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 14 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 14 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 14 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 14 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = d2cases_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Change in change in cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in change of cases, 14 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = d2cases_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Change in change in cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in change of cases, 14 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving14_mov, color="Leaving home")) +
geom_line(aes(y = dlog_cases_mov*100-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~(.+30)/100, name = "Change in log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 14 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in log of cases, 14 day lag")
10 day lag
# 10 day lag
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving10_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 10 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - case growth rate, 10 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving10_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 10 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - case growth rate, 10 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving10_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 10 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 10 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving10_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 10 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 10 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving10_mov, color="Leaving home")) +
geom_line(aes(y = dlog_cases_mov*100-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~(.+30)/100, name = "Change in log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 10 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in log of cases, 10 day lag")
18 day lag
# 18 day lag
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving18_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 18 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 18 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving18_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 18 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 18 Day Lag, pre 4/15")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving18_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 18 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 18 Day Lag")
scc_test_big_pre415 %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving18_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 18 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 18 Day Lag, pre 4/15")
21 day lag
# 21 day lag??
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving21_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 21 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 21 Day Lag")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving21_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 21 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 21 Day Lag")
28 day lag…
# going wild with a 28 day lag
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving28_mov, color="Leaving home")) +
geom_line(aes(y = cases_growth_daily_mov-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+30, name = "Daily case growth rate (%), 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 28 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - growth rate, 28 Day Lag")
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving28_mov, color="Leaving home")) +
geom_line(aes(y = dcases_mov-40, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~.*1+40, name = "Daily new cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 28 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in cases, 28 Day Lag")
Just log, 7 days
# trying just log plot with 7 day lag
scc_test_big %>% ggplot(
aes(x = date)) +
geom_line(aes(y = leaving7_mov, color="Leaving home")) +
geom_line(aes(y = dlog_cases_mov*100-30, color = "Cases")) +
scale_y_continuous(sec.axis = sec_axis(~(.+30)/100, name = "Change in log of cases, 7 day moving average")) +
scale_colour_manual(values = c("red", "blue")) +
labs(y = "Change in percent of devices leaving home 7 days ago relative to before, 7 day moving average", x = "Date", color = "Data", title = "Santa Clara County - change in log of cases, 7 day lag")
Testing ardlm
dleaving and d2logcases for 4 lags, moving average
testing_ardl4 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c()))
summary(testing_ardl4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0210782 -0.0014117 0.0005584 0.0017166 0.0262660
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0011101 0.0009779 -1.135 0.260484
## X.4 -0.0007008 0.0012865 -0.545 0.587818
## Y.1 -0.0481037 0.1246589 -0.386 0.700844
## Y.2 0.2334984 0.1275092 1.831 0.071650 .
## Y.3 0.4307959 0.1059047 4.068 0.000131 ***
## Y.4 0.0513601 0.1156449 0.444 0.658432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.007328 on 65 degrees of freedom
## Multiple R-squared: 0.2442, Adjusted R-squared: 0.1861
## F-statistic: 4.201 on 5 and 65 DF, p-value: 0.002272
testing_ardl4_1 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2)))
summary(testing_ardl4_1)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0191964 -0.0025874 0.0008812 0.0016298 0.0301641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0014823 0.0009735 -1.523 0.132628
## X.4 0.0006929 0.0010556 0.656 0.513845
## Y.1 -0.1230445 0.1198310 -1.027 0.308255
## Y.3 0.3902563 0.1053953 3.703 0.000437 ***
## Y.4 0.0411679 0.1175523 0.350 0.727297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.007458 on 66 degrees of freedom
## Multiple R-squared: 0.2053, Adjusted R-squared: 0.1571
## F-statistic: 4.261 on 4 and 66 DF, p-value: 0.003964
testing_ardl4_2 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3)))
summary(testing_ardl4_2)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025457 -0.003211 0.000477 0.002497 0.038036
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002030 0.001050 -1.934 0.0574 .
## X.4 0.001812 0.001103 1.642 0.1052
## Y.1 -0.088357 0.130304 -0.678 0.5001
## Y.4 -0.035488 0.126215 -0.281 0.7794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008135 on 67 degrees of freedom
## Multiple R-squared: 0.04016, Adjusted R-squared: -0.002818
## F-statistic: 0.9344 on 3 and 67 DF, p-value: 0.429
testing_ardl4_3 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_3)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025727 -0.003421 0.000533 0.002464 0.037708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002016 0.001041 -1.936 0.057 .
## X.4 0.001711 0.001036 1.652 0.103
## Y.1 -0.099093 0.123738 -0.801 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008079 on 68 degrees of freedom
## Multiple R-squared: 0.03903, Adjusted R-squared: 0.01076
## F-statistic: 1.381 on 2 and 68 DF, p-value: 0.2583
testing_ardl4_4 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(), q=c(2,3,4)))
summary(testing_ardl4_4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0132454 -0.0034096 -0.0004879 0.0035227 0.0193740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0014412 0.0008493 -1.697 0.09458 .
## X.t -0.0006620 0.0016091 -0.411 0.68215
## X.1 0.0010576 0.0019109 0.553 0.58187
## X.2 0.0050034 0.0015057 3.323 0.00148 **
## X.3 0.0027778 0.0016091 1.726 0.08911 .
## X.4 -0.0036490 0.0013051 -2.796 0.00682 **
## Y.1 -0.2350380 0.1121475 -2.096 0.04006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006421 on 64 degrees of freedom
## Multiple R-squared: 0.4288, Adjusted R-squared: 0.3752
## F-statistic: 8.006 on 6 and 64 DF, p-value: 1.847e-06
testing_ardl4_5 = ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_5)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025727 -0.003421 0.000533 0.002464 0.037708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002016 0.001041 -1.936 0.057 .
## X.4 0.001711 0.001036 1.652 0.103
## Y.1 -0.099093 0.123738 -0.801 0.426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.008079 on 68 degrees of freedom
## Multiple R-squared: 0.03903, Adjusted R-squared: 0.01076
## F-statistic: 1.381 on 2 and 68 DF, p-value: 0.2583
testing_ardl4_6= ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1,2), q=c(2,3,4)))
summary(testing_ardl4_6)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0143467 -0.0040547 -0.0003573 0.0026258 0.0311176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0018496 0.0009207 -2.009 0.0486 *
## X.3 0.0064126 0.0014294 4.486 2.92e-05 ***
## X.4 -0.0024872 0.0013087 -1.900 0.0617 .
## Y.1 -0.3167641 0.1196011 -2.649 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.007138 on 67 degrees of freedom
## Multiple R-squared: 0.261, Adjusted R-squared: 0.2279
## F-statistic: 7.888 on 3 and 67 DF, p-value: 0.0001395
testing_ardl4_7= ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0,1), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0138842 -0.0032318 -0.0004221 0.0034649 0.0189539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0014244 0.0008233 -1.730 0.08831 .
## X.2 0.0052557 0.0012066 4.356 4.73e-05 ***
## X.3 0.0026571 0.0015345 1.732 0.08802 .
## X.4 -0.0033944 0.0011806 -2.875 0.00543 **
## Y.1 -0.2232091 0.1083528 -2.060 0.04334 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006338 on 66 degrees of freedom
## Multiple R-squared: 0.426, Adjusted R-squared: 0.3912
## F-statistic: 12.25 on 4 and 66 DF, p-value: 1.666e-07
testing_ardl4_7= ardlDlm(x = scc_test_big$dleaving_mov, y = scc_test_big$d2log_cases_mov, p = 4, q = 4, remove = list(p = c(0), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.013724 -0.003323 -0.000673 0.003586 0.019547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0013885 0.0008342 -1.664 0.10084
## X.1 0.0005304 0.0014084 0.377 0.70770
## X.2 0.0049330 0.0014863 3.319 0.00148 **
## X.3 0.0026136 0.0015488 1.687 0.09630 .
## X.4 -0.0034401 0.0011946 -2.880 0.00538 **
## Y.1 -0.2309722 0.1109951 -2.081 0.04139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.00638 on 65 degrees of freedom
## Multiple R-squared: 0.4273, Adjusted R-squared: 0.3832
## F-statistic: 9.698 on 5 and 65 DF, p-value: 5.948e-07
dleaving and d2logcases for 4 lags, no moving average
testing_ardl4 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c()))
summary(testing_ardl4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.108594 -0.011692 0.001610 0.009945 0.095411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006197 0.004251 -1.457 0.14979
## X.4 0.002068 0.001347 1.535 0.12960
## Y.1 -0.679648 0.121205 -5.607 4.52e-07 ***
## Y.2 -0.568728 0.133252 -4.268 6.54e-05 ***
## Y.3 -0.438087 0.138566 -3.162 0.00238 **
## Y.4 -0.190597 0.101444 -1.879 0.06475 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03489 on 65 degrees of freedom
## Multiple R-squared: 0.351, Adjusted R-squared: 0.3011
## F-statistic: 7.03 on 5 and 65 DF, p-value: 2.634e-05
testing_ardl4_1 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2)))
summary(testing_ardl4_1)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.144764 -0.009669 0.001171 0.006960 0.153343
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002752 0.004687 -0.587 0.559
## X.4 0.001073 0.001490 0.720 0.474
## Y.1 -0.385537 0.111962 -3.443 0.001 **
## Y.3 -0.037851 0.114549 -0.330 0.742
## Y.4 -0.022127 0.104933 -0.211 0.834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03918 on 66 degrees of freedom
## Multiple R-squared: 0.1691, Adjusted R-squared: 0.1187
## F-statistic: 3.358 on 4 and 66 DF, p-value: 0.01457
testing_ardl4_2 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3)))
summary(testing_ardl4_2)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149466 -0.009126 0.002070 0.006699 0.150983
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0026514 0.0046461 -0.571 0.570124
## X.4 0.0009747 0.0014499 0.672 0.503710
## Y.1 -0.3824361 0.1108240 -3.451 0.000972 ***
## Y.4 0.0038177 0.0691463 0.055 0.956134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03892 on 67 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1304
## F-statistic: 4.5 on 3 and 67 DF, p-value: 0.006162
testing_ardl4_3 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_3)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149226 -0.009177 0.002010 0.006665 0.151077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002660 0.004609 -0.577 0.56578
## X.4 0.001011 0.001280 0.790 0.43232
## Y.1 -0.383527 0.108246 -3.543 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03863 on 68 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1432
## F-statistic: 6.849 on 2 and 68 DF, p-value: 0.00195
testing_ardl4_4 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(), q=c(2,3,4)))
summary(testing_ardl4_4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.115511 -0.016402 -0.004825 0.008661 0.125574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0014619 0.0042703 0.342 0.733215
## X.t 0.0053683 0.0013287 4.040 0.000146 ***
## X.1 0.0037749 0.0014952 2.525 0.014068 *
## X.2 0.0017172 0.0013293 1.292 0.201080
## X.3 -0.0003821 0.0012559 -0.304 0.761943
## X.4 0.0004277 0.0012282 0.348 0.728798
## Y.1 -0.4519569 0.1099907 -4.109 0.000115 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03442 on 64 degrees of freedom
## Multiple R-squared: 0.3782, Adjusted R-squared: 0.3199
## F-statistic: 6.489 on 6 and 64 DF, p-value: 2.207e-05
testing_ardl4_5 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_5)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149226 -0.009177 0.002010 0.006665 0.151077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002660 0.004609 -0.577 0.56578
## X.4 0.001011 0.001280 0.790 0.43232
## Y.1 -0.383527 0.108246 -3.543 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03863 on 68 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1432
## F-statistic: 6.849 on 2 and 68 DF, p-value: 0.00195
testing_ardl4_6= ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1,2), q=c(2,3,4)))
summary(testing_ardl4_6)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149260 -0.009174 0.002009 0.006684 0.151095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.655e-03 4.663e-03 -0.569 0.57106
## X.3 1.638e-05 1.338e-03 0.012 0.99027
## X.4 1.014e-03 1.304e-03 0.777 0.43970
## Y.1 -3.836e-01 1.091e-01 -3.516 0.00079 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03892 on 67 degrees of freedom
## Multiple R-squared: 0.1677, Adjusted R-squared: 0.1304
## F-statistic: 4.499 on 3 and 67 DF, p-value: 0.00617
testing_ardl4_7= ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0,1), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149096 -0.009543 0.001530 0.006562 0.151765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.568e-03 4.755e-03 -0.540 0.590999
## X.2 1.743e-04 1.466e-03 0.119 0.905735
## X.3 3.586e-05 1.358e-03 0.026 0.979005
## X.4 1.054e-03 1.358e-03 0.777 0.440161
## Y.1 -3.844e-01 1.101e-01 -3.490 0.000865 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03921 on 66 degrees of freedom
## Multiple R-squared: 0.1679, Adjusted R-squared: 0.1174
## F-statistic: 3.328 on 4 and 66 DF, p-value: 0.01521
testing_ardl4_7= ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 4, q = 4, remove = list(p = c(0), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143175 -0.013751 -0.000918 0.011317 0.153910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0010639 0.0046959 -0.227 0.821484
## X.1 0.0034469 0.0016596 2.077 0.041768 *
## X.2 0.0006313 0.0014472 0.436 0.664135
## X.3 0.0007070 0.0013635 0.519 0.605841
## X.4 0.0003697 0.0013652 0.271 0.787381
## Y.1 -0.5020844 0.1214902 -4.133 0.000105 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03826 on 65 degrees of freedom
## Multiple R-squared: 0.2196, Adjusted R-squared: 0.1596
## F-statistic: 3.659 on 5 and 65 DF, p-value: 0.005597
6 lags
testing_ardl6 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0,1,2,3,4,5), q=c()))
summary(testing_ardl6)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.138345 -0.013401 0.004258 0.013870 0.144748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006730 0.004282 -1.572 0.1208
## X.6 -0.002287 0.001204 -1.900 0.0618 .
## Y.1 -0.553200 0.106851 -5.177 2.3e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03518 on 66 degrees of freedom
## Multiple R-squared: 0.2915, Adjusted R-squared: 0.27
## F-statistic: 13.58 on 2 and 66 DF, p-value: 1.151e-05
testing_ardl6_1 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0,1,2,3,4), q=c()))
summary(testing_ardl6_1)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12561 -0.01297 0.00249 0.01357 0.14795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007181 0.004224 -1.700 0.0939 .
## X.5 -0.002117 0.001209 -1.751 0.0847 .
## X.6 -0.002518 0.001192 -2.112 0.0386 *
## Y.1 -0.525211 0.106426 -4.935 5.87e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03464 on 65 degrees of freedom
## Multiple R-squared: 0.3234, Adjusted R-squared: 0.2922
## F-statistic: 10.36 on 3 and 65 DF, p-value: 1.167e-05
testing_ardl6_2 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0,1,2,3), q=c()))
summary(testing_ardl6_2)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.126507 -0.012434 0.003494 0.014056 0.147071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0068783 0.0043102 -1.596 0.1155
## X.4 0.0005629 0.0013229 0.426 0.6719
## X.5 -0.0020670 0.0012226 -1.691 0.0958 .
## X.6 -0.0023652 0.0012527 -1.888 0.0636 .
## Y.1 -0.5176719 0.1085583 -4.769 1.11e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03486 on 64 degrees of freedom
## Multiple R-squared: 0.3253, Adjusted R-squared: 0.2831
## F-statistic: 7.714 on 4 and 64 DF, p-value: 3.879e-05
testing_ardl6_3 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0,1,2), q=c()))
summary(testing_ardl6_3)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124346 -0.010249 0.006063 0.014241 0.143227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0078112 0.0043238 -1.807 0.0756 .
## X.3 -0.0019079 0.0013274 -1.437 0.1556
## X.4 0.0003701 0.0013188 0.281 0.7799
## X.5 -0.0024086 0.0012356 -1.949 0.0557 .
## X.6 -0.0020294 0.0012642 -1.605 0.1134
## Y.1 -0.5179871 0.1076656 -4.811 9.74e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03458 on 63 degrees of freedom
## Multiple R-squared: 0.3467, Adjusted R-squared: 0.2949
## F-statistic: 6.688 on 5 and 63 DF, p-value: 4.696e-05
testing_ardl6_4 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0,1), q=c()))
summary(testing_ardl6_4)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124608 -0.010532 0.006042 0.014207 0.142690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0078768 0.0044209 -1.782 0.0797 .
## X.2 -0.0001192 0.0013503 -0.088 0.9299
## X.3 -0.0019226 0.0013483 -1.426 0.1589
## X.4 0.0003437 0.0013624 0.252 0.8017
## X.5 -0.0023847 0.0012747 -1.871 0.0661 .
## X.6 -0.0020286 0.0012743 -1.592 0.1165
## Y.1 -0.5181515 0.1085396 -4.774 1.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03485 on 62 degrees of freedom
## Multiple R-squared: 0.3468, Adjusted R-squared: 0.2836
## F-statistic: 5.487 on 6 and 62 DF, p-value: 0.0001318
testing_ardl6_5 = ardlDlm(x = scc_test_big$dleaving, y = scc_test_big$d2log_cases, p = 6, q = 1, remove = list(p = c(0), q=c()))
summary(testing_ardl6_5)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124036 -0.014554 0.001443 0.014035 0.143189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0064468 0.0043898 -1.469 0.1471
## X.1 0.0029219 0.0015137 1.930 0.0582 .
## X.2 0.0002299 0.0013339 0.172 0.8637
## X.3 -0.0014191 0.0013451 -1.055 0.2956
## X.4 -0.0001975 0.0013626 -0.145 0.8852
## X.5 -0.0022017 0.0012512 -1.760 0.0835 .
## X.6 -0.0015787 0.0012687 -1.244 0.2181
## Y.1 -0.6116081 0.1167423 -5.239 2.12e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03411 on 61 degrees of freedom
## Multiple R-squared: 0.3844, Adjusted R-squared: 0.3138
## F-statistic: 5.442 on 7 and 61 DF, p-value: 6.926e-05
leaving and dcases for 4 lags
# keeping only 4 x lags, removing different y lags
testing_ardl4 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c()))
summary(testing_ardl4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.879 -10.171 0.751 6.640 42.219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.46602 4.29030 1.973 0.052717 .
## X.4 0.12422 0.16660 0.746 0.458587
## Y.1 0.49848 0.12341 4.039 0.000144 ***
## Y.2 0.18318 0.13764 1.331 0.187876
## Y.3 0.08556 0.13788 0.621 0.537064
## Y.4 0.06895 0.12327 0.559 0.577825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.38 on 65 degrees of freedom
## Multiple R-squared: 0.5633, Adjusted R-squared: 0.5298
## F-statistic: 16.77 on 5 and 65 DF, p-value: 1.302e-10
testing_ardl4_1 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2)))
summary(testing_ardl4_1)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.422 -9.052 -0.986 6.962 41.272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.84217 4.30592 2.053 0.044 *
## X.4 0.11460 0.16742 0.685 0.496
## Y.1 0.57353 0.11041 5.195 2.15e-06 ***
## Y.3 0.14885 0.13017 1.143 0.257
## Y.4 0.09642 0.12223 0.789 0.433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.47 on 66 degrees of freedom
## Multiple R-squared: 0.5514, Adjusted R-squared: 0.5243
## F-statistic: 20.28 on 4 and 66 DF, p-value: 6.212e-11
testing_ardl4_2 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3)))
summary(testing_ardl4_2)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.285 -11.347 -2.101 6.693 43.230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.17858 4.30571 2.132 0.0367 *
## X.4 0.09499 0.16692 0.569 0.5712
## Y.1 0.62296 0.10182 6.118 5.53e-08 ***
## Y.4 0.17307 0.10245 1.689 0.0958 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.5 on 67 degrees of freedom
## Multiple R-squared: 0.5426, Adjusted R-squared: 0.5221
## F-statistic: 26.49 on 3 and 67 DF, p-value: 2.059e-11
testing_ardl4_3 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_3)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.147 -8.903 0.100 6.314 51.611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98698 4.33697 2.303 0.0244 *
## X.4 0.01763 0.16268 0.108 0.9140
## Y.1 0.71922 0.08553 8.409 3.96e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.7 on 68 degrees of freedom
## Multiple R-squared: 0.5231, Adjusted R-squared: 0.509
## F-statistic: 37.29 on 2 and 68 DF, p-value: 1.168e-11
# all x lags, only 1 y lag
testing_ardl4_4 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(), q=c(2,3,4)))
summary(testing_ardl4_4)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.240 -9.202 -0.652 7.991 47.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.32048 4.81150 2.145 0.0358 *
## X.t 1.07698 0.59973 1.796 0.0772 .
## X.1 -1.20822 0.74111 -1.630 0.1080
## X.2 -0.01992 0.70538 -0.028 0.9776
## X.3 -0.59968 0.73693 -0.814 0.4188
## X.4 0.79540 0.52516 1.515 0.1348
## Y.1 0.72983 0.08685 8.403 6.28e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.56 on 64 degrees of freedom
## Multiple R-squared: 0.5591, Adjusted R-squared: 0.5178
## F-statistic: 13.53 on 6 and 64 DF, p-value: 7.627e-10
# removing x lags with only 1 y lag
testing_ardl4_5 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2,3), q=c(2,3,4)))
summary(testing_ardl4_5)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.147 -8.903 0.100 6.314 51.611
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.98698 4.33697 2.303 0.0244 *
## X.4 0.01763 0.16268 0.108 0.9140
## Y.1 0.71922 0.08553 8.409 3.96e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.7 on 68 degrees of freedom
## Multiple R-squared: 0.5231, Adjusted R-squared: 0.509
## F-statistic: 37.29 on 2 and 68 DF, p-value: 1.168e-11
testing_ardl4_6= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1,2), q=c(2,3,4)))
summary(testing_ardl4_6)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.562 -9.924 -0.161 7.384 51.324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.49569 4.36412 2.176 0.0331 *
## X.3 -0.51485 0.51233 -1.005 0.3185
## X.4 0.49779 0.50474 0.986 0.3276
## Y.1 0.70739 0.08633 8.194 1.08e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.69 on 67 degrees of freedom
## Multiple R-squared: 0.5302, Adjusted R-squared: 0.5091
## F-statistic: 25.2 on 3 and 67 DF, p-value: 4.989e-11
testing_ardl4_7= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0,1), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.768 -10.755 0.559 7.286 50.315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.75386 4.48529 1.952 0.0552 .
## X.2 -0.41619 0.54730 -0.760 0.4497
## X.3 -0.14876 0.70420 -0.211 0.8333
## X.4 0.51672 0.50694 1.019 0.3118
## Y.1 0.70415 0.08671 8.121 1.61e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.74 on 66 degrees of freedom
## Multiple R-squared: 0.5342, Adjusted R-squared: 0.506
## F-statistic: 18.93 on 4 and 66 DF, p-value: 2.088e-10
# looking at different y lags included on their own with all x lags
testing_ardl4_7= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0), q=c(2,3,4)))
summary(testing_ardl4_7)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.154 -10.949 0.748 7.768 50.001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.92920 4.70203 1.686 0.0965 .
## X.1 -0.35578 0.57879 -0.615 0.5409
## X.2 -0.13593 0.71434 -0.190 0.8497
## X.3 -0.16636 0.70812 -0.235 0.8150
## X.4 0.58157 0.52016 1.118 0.2677
## Y.1 0.70417 0.08712 8.083 2.08e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.81 on 65 degrees of freedom
## Multiple R-squared: 0.5369, Adjusted R-squared: 0.5013
## F-statistic: 15.07 on 5 and 65 DF, p-value: 8.202e-10
testing_ardl4_8= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0), q=c(1,2,3)))
summary(testing_ardl4_8)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.680 -11.561 -3.558 8.674 42.927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.16022 5.43298 3.343 0.00138 **
## X.1 0.25293 0.72785 0.348 0.72933
## X.2 -0.00311 0.88561 -0.004 0.99721
## X.3 -0.51994 0.87323 -0.595 0.55363
## X.4 0.34027 0.65018 0.523 0.60251
## Y.4 0.52383 0.11615 4.510 2.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.3 on 65 degrees of freedom
## Multiple R-squared: 0.2928, Adjusted R-squared: 0.2384
## F-statistic: 5.382 on 5 and 65 DF, p-value: 0.0003351
testing_ardl4_9= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0), q=c(1,2,4)))
summary(testing_ardl4_9)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -37.008 -11.162 -1.654 8.059 47.072
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.8355 5.1871 3.053 0.00328 **
## X.1 0.5940 0.6958 0.854 0.39640
## X.2 -0.3610 0.8320 -0.434 0.66577
## X.3 -0.9508 0.8269 -1.150 0.25440
## X.4 0.7947 0.6054 1.313 0.19389
## Y.3 0.5948 0.1069 5.562 5.4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.26 on 65 degrees of freedom
## Multiple R-squared: 0.3709, Adjusted R-squared: 0.3225
## F-statistic: 7.664 on 5 and 65 DF, p-value: 1.032e-05
testing_ardl4_10= ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dcases, p = 4, q = 4, remove = list(p = c(0), q=c(1,3,4)))
summary(testing_ardl4_10)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.398 -9.452 -3.152 6.970 37.324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.31430 4.99741 2.464 0.0164 *
## X.1 0.41075 0.64480 0.637 0.5264
## X.2 -0.88803 0.78640 -1.129 0.2630
## X.3 -0.21414 0.77593 -0.276 0.7834
## X.4 0.69396 0.56946 1.219 0.2274
## Y.2 0.63891 0.09684 6.597 8.87e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.23 on 65 degrees of freedom
## Multiple R-squared: 0.4439, Adjusted R-squared: 0.4011
## F-statistic: 10.38 on 5 and 65 DF, p-value: 2.408e-07
leaving and dlog_cases for up to 5 lags
# all y lags, only x lag of 5
testing_ardl5 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,4), q=c()))
summary(testing_ardl5)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.108165 -0.006456 -0.000218 0.007674 0.112385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0052952 0.0283289 0.187 0.85233
## X.5 0.0002029 0.0008899 0.228 0.82035
## Y.1 0.2948595 0.1108391 2.660 0.00989 **
## Y.2 0.2168115 0.1125628 1.926 0.05860 .
## Y.3 -0.0618693 0.1100965 -0.562 0.57614
## Y.4 0.4044801 0.0920751 4.393 4.37e-05 ***
## Y.5 0.0186078 0.0878808 0.212 0.83299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03129 on 63 degrees of freedom
## Multiple R-squared: 0.8445, Adjusted R-squared: 0.8297
## F-statistic: 57.01 on 6 and 63 DF, p-value: < 2.2e-16
# all x lags, only y lag 1
testing_ardl5_1 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(), q=c(2,3,4,5)))
summary(testing_ardl5_1)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.113344 -0.016694 -0.003508 0.018559 0.085327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.307e-01 2.789e-02 4.685 1.58e-05 ***
## X.t 4.901e-03 1.329e-03 3.687 0.000479 ***
## X.1 -1.928e-03 1.764e-03 -1.093 0.278660
## X.2 9.720e-05 1.619e-03 0.060 0.952311
## X.3 -2.010e-03 1.628e-03 -1.234 0.221718
## X.4 3.053e-03 1.637e-03 1.865 0.066936 .
## X.5 6.047e-05 1.213e-03 0.050 0.960396
## Y.1 4.210e-01 1.159e-01 3.632 0.000572 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03179 on 62 degrees of freedom
## Multiple R-squared: 0.8421, Adjusted R-squared: 0.8242
## F-statistic: 47.23 on 7 and 62 DF, p-value: < 2.2e-16
# all x lags, only y lag 2
testing_ardl5_2 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(), q=c(1,3,4,5)))
summary(testing_ardl5_2)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141281 -0.014170 -0.003306 0.012675 0.069068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.127736 0.024900 5.130 3.08e-06 ***
## X.t 0.004515 0.001274 3.544 0.000757 ***
## X.1 0.001248 0.001586 0.787 0.434225
## X.2 -0.002163 0.001654 -1.308 0.195630
## X.3 -0.002021 0.001575 -1.283 0.204126
## X.4 0.001881 0.001576 1.194 0.236968
## X.5 0.000671 0.001131 0.593 0.555309
## Y.2 0.454660 0.106670 4.262 7.01e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03078 on 62 degrees of freedom
## Multiple R-squared: 0.8519, Adjusted R-squared: 0.8352
## F-statistic: 50.94 on 7 and 62 DF, p-value: < 2.2e-16
# adding in x lags
testing_ardl5_3 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0), q=c(1,3,4,5)))
summary(testing_ardl5_3)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.150291 -0.015545 -0.004782 0.012848 0.083301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1188892 0.0269508 4.411 4.09e-05 ***
## X.1 0.0045552 0.0013949 3.266 0.00177 **
## X.2 -0.0024366 0.0017970 -1.356 0.17996
## X.3 -0.0002658 0.0016261 -0.163 0.87067
## X.4 0.0014825 0.0017096 0.867 0.38914
## X.5 0.0004425 0.0012288 0.360 0.71995
## Y.2 0.4278442 0.1157496 3.696 0.00046 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03349 on 63 degrees of freedom
## Multiple R-squared: 0.8219, Adjusted R-squared: 0.8049
## F-statistic: 48.45 on 6 and 63 DF, p-value: < 2.2e-16
testing_ardl5_4 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1), q=c(1,3,4,5)))
summary(testing_ardl5_4)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15618 -0.01633 -0.00478 0.01459 0.08576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1216601 0.0288998 4.210 8.14e-05 ***
## X.2 0.0013979 0.0014594 0.958 0.34174
## X.3 -0.0006372 0.0017403 -0.366 0.71548
## X.4 0.0034289 0.0017191 1.995 0.05035 .
## X.5 -0.0003827 0.0012901 -0.297 0.76771
## Y.2 0.3586927 0.1220864 2.938 0.00459 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03593 on 64 degrees of freedom
## Multiple R-squared: 0.7917, Adjusted R-squared: 0.7755
## F-statistic: 48.66 on 5 and 64 DF, p-value: < 2.2e-16
testing_ardl5_5 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2), q=c(1,3,4,5)))
summary(testing_ardl5_5)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155993 -0.014162 -0.004484 0.013365 0.092244
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1116142 0.0269125 4.147 9.95e-05 ***
## X.3 0.0003372 0.0014112 0.239 0.81191
## X.4 0.0033626 0.0017166 1.959 0.05442 .
## X.5 -0.0002151 0.0012774 -0.168 0.86681
## Y.2 0.3906141 0.1173752 3.328 0.00144 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03591 on 65 degrees of freedom
## Multiple R-squared: 0.7887, Adjusted R-squared: 0.7757
## F-statistic: 60.67 on 4 and 65 DF, p-value: < 2.2e-16
testing_ardl5_6 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3), q=c(1,3,4,5)))
summary(testing_ardl5_6)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155160 -0.013831 -0.004086 0.013223 0.094281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.109047 0.024498 4.451 3.37e-05 ***
## X.4 0.003630 0.001292 2.810 0.006518 **
## X.5 -0.000228 0.001267 -0.180 0.857751
## Y.2 0.399896 0.109967 3.636 0.000542 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03565 on 66 degrees of freedom
## Multiple R-squared: 0.7886, Adjusted R-squared: 0.779
## F-statistic: 82.05 on 3 and 66 DF, p-value: < 2.2e-16
testing_ardl5_7 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,4), q=c(1,3,4,5)))
summary(testing_ardl5_7)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.156345 -0.012802 -0.005804 0.014654 0.128303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0856371 0.0241939 3.540 0.000734 ***
## X.5 0.0026401 0.0007884 3.349 0.001335 **
## Y.2 0.4897097 0.1105002 4.432 3.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03744 on 67 degrees of freedom
## Multiple R-squared: 0.7633, Adjusted R-squared: 0.7562
## F-statistic: 108 on 2 and 67 DF, p-value: < 2.2e-16
# trying different single y lags with only one x lag (4)
testing_ardl5_9 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,5), q=c(2,3,4,5)))
summary(testing_ardl5_9)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.128827 -0.014932 -0.007603 0.010000 0.119612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1018230 0.0233030 4.370 4.36e-05 ***
## X.4 0.0031757 0.0007615 4.170 8.81e-05 ***
## Y.1 0.4701150 0.1064001 4.418 3.66e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03624 on 68 degrees of freedom
## Multiple R-squared: 0.7941, Adjusted R-squared: 0.788
## F-statistic: 131.1 on 2 and 68 DF, p-value: < 2.2e-16
testing_ardl5_10 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,5), q=c(1,3,4,5)))
summary(testing_ardl5_10)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.159248 -0.014228 -0.005846 0.012120 0.128545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1261810 0.0251299 5.021 3.94e-06 ***
## X.4 0.0039389 0.0008157 4.829 8.14e-06 ***
## Y.2 0.3462031 0.1142744 3.030 0.00346 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03859 on 68 degrees of freedom
## Multiple R-squared: 0.7665, Adjusted R-squared: 0.7596
## F-statistic: 111.6 on 2 and 68 DF, p-value: < 2.2e-16
testing_ardl5_11 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,5), q=c(1,2,4,5)))
summary(testing_ardl5_11)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.146026 -0.014964 -0.005749 0.011137 0.116249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1333212 0.0238245 5.596 4.26e-07 ***
## X.4 0.0041698 0.0007751 5.380 9.93e-07 ***
## Y.3 0.2956037 0.1018290 2.903 0.00498 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03878 on 68 degrees of freedom
## Multiple R-squared: 0.7642, Adjusted R-squared: 0.7573
## F-statistic: 110.2 on 2 and 68 DF, p-value: < 2.2e-16
testing_ardl5_12 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,5), q=c(1,2,3,5)))
summary(testing_ardl5_12)
##
## Time series regression with "ts" data:
## Start = 5, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.125928 -0.016057 -0.007117 0.013790 0.156061
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1387167 0.0233280 5.946 1.05e-07 ***
## X.4 0.0043383 0.0007603 5.706 2.75e-07 ***
## Y.4 0.2720220 0.0997752 2.726 0.00814 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03903 on 68 degrees of freedom
## Multiple R-squared: 0.7611, Adjusted R-squared: 0.7541
## F-statistic: 108.3 on 2 and 68 DF, p-value: < 2.2e-16
testing_ardl5_13 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,1,2,3,5), q=c(1,2,3,4)))
summary(testing_ardl5_13)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.112131 -0.017279 -0.006861 0.012955 0.124604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1532479 0.0189906 8.070 1.80e-11 ***
## X.4 0.0048166 0.0006197 7.773 6.18e-11 ***
## Y.5 0.1865965 0.0809860 2.304 0.0243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03745 on 67 degrees of freedom
## Multiple R-squared: 0.7631, Adjusted R-squared: 0.756
## F-statistic: 107.9 on 2 and 67 DF, p-value: < 2.2e-16
# compare to just having different x values on their own with single y lag
testing_ardl5_14 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,4,2,3,5), q=c(1,2,3,4)))
summary(testing_ardl5_14)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.100868 -0.017362 -0.007354 0.019674 0.113587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1576405 0.0164726 9.570 3.69e-14 ***
## X.1 0.0050528 0.0005436 9.295 1.14e-13 ***
## Y.5 0.2855592 0.0630975 4.526 2.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03413 on 67 degrees of freedom
## Multiple R-squared: 0.8032, Adjusted R-squared: 0.7973
## F-statistic: 136.7 on 2 and 67 DF, p-value: < 2.2e-16
testing_ardl5_15 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,4,1,3,5), q=c(1,2,3,4)))
summary(testing_ardl5_15)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.114858 -0.018717 -0.007667 0.019610 0.115542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1482808 0.0181065 8.189 1.10e-11 ***
## X.2 0.0046910 0.0005938 7.899 3.65e-11 ***
## Y.5 0.2712425 0.0722627 3.754 0.000367 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03716 on 67 degrees of freedom
## Multiple R-squared: 0.7667, Adjusted R-squared: 0.7598
## F-statistic: 110.1 on 2 and 67 DF, p-value: < 2.2e-16
testing_ardl5_16 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,4,1,2,5), q=c(1,2,3,4)))
summary(testing_ardl5_16)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.116827 -0.018435 -0.007668 0.008932 0.118335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1437949 0.0180533 7.965 2.78e-11 ***
## X.3 0.0045355 0.0005909 7.675 9.27e-11 ***
## Y.5 0.2565209 0.0751181 3.415 0.00109 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03767 on 67 degrees of freedom
## Multiple R-squared: 0.7603, Adjusted R-squared: 0.7531
## F-statistic: 106.2 on 2 and 67 DF, p-value: < 2.2e-16
testing_ardl5_17 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 5, q = 5, remove = list(p = c(0,4,1,2,3), q=c(1,2,3,4)))
summary(testing_ardl5_17)
##
## Time series regression with "ts" data:
## Start = 6, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.112037 -0.021885 -0.009319 0.013639 0.167204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1552352 0.0253111 6.133 5.2e-08 ***
## X.5 0.0047860 0.0008219 5.823 1.8e-07 ***
## Y.5 0.1354347 0.1083249 1.250 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04208 on 67 degrees of freedom
## Multiple R-squared: 0.7009, Adjusted R-squared: 0.6919
## F-statistic: 78.49 on 2 and 67 DF, p-value: < 2.2e-16
Compare with up to 10 lags in x, 5 in y
# all y, all x
testing_ardl10 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(), q=c()))
summary(testing_ardl10)
##
## Time series regression with "ts" data:
## Start = 11, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.045122 -0.008367 -0.001981 0.009459 0.038077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.538e-02 2.831e-02 2.663 0.010509 *
## X.t 2.981e-03 9.597e-04 3.106 0.003176 **
## X.1 8.409e-04 1.102e-03 0.763 0.449151
## X.2 9.294e-05 1.082e-03 0.086 0.931907
## X.3 -1.030e-03 1.116e-03 -0.923 0.360576
## X.4 -1.004e-03 1.102e-03 -0.912 0.366544
## X.5 -5.883e-04 1.161e-03 -0.507 0.614625
## X.6 -4.386e-04 1.013e-03 -0.433 0.666868
## X.7 2.568e-03 1.035e-03 2.482 0.016635 *
## X.8 -1.772e-04 1.079e-03 -0.164 0.870274
## X.9 -1.240e-03 1.016e-03 -1.220 0.228265
## X.10 5.826e-04 8.453e-04 0.689 0.493973
## Y.1 3.158e-01 1.379e-01 2.289 0.026522 *
## Y.2 1.435e-01 1.184e-01 1.212 0.231598
## Y.3 -1.080e-01 1.003e-01 -1.076 0.287120
## Y.4 3.430e-01 9.358e-02 3.666 0.000616 ***
## Y.5 1.626e-02 8.803e-02 0.185 0.854268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01761 on 48 degrees of freedom
## Multiple R-squared: 0.9431, Adjusted R-squared: 0.9241
## F-statistic: 49.69 on 16 and 48 DF, p-value: < 2.2e-16
# all x, only one y
testing_ardl10_1 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(), q=c(1,2,3,4)))
summary(testing_ardl10_1)
##
## Time series regression with "ts" data:
## Start = 11, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.037816 -0.013624 -0.002767 0.009762 0.076898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.719e-01 2.258e-02 7.614 5.21e-10 ***
## X.t 2.544e-03 1.163e-03 2.188 0.0332 *
## X.1 1.453e-03 1.299e-03 1.119 0.2685
## X.2 1.288e-03 1.295e-03 0.994 0.3247
## X.3 -1.489e-03 1.297e-03 -1.147 0.2565
## X.4 8.663e-04 1.258e-03 0.688 0.4943
## X.5 -1.900e-03 1.428e-03 -1.330 0.1894
## X.6 -5.141e-05 1.238e-03 -0.042 0.9670
## X.7 1.213e-03 1.269e-03 0.956 0.3437
## X.8 1.680e-03 1.243e-03 1.351 0.1825
## X.9 -1.255e-03 1.253e-03 -1.002 0.3212
## X.10 1.354e-03 9.629e-04 1.406 0.1656
## Y.5 2.444e-01 8.815e-02 2.773 0.0077 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0223 on 52 degrees of freedom
## Multiple R-squared: 0.9011, Adjusted R-squared: 0.8783
## F-statistic: 39.5 on 12 and 52 DF, p-value: < 2.2e-16
# testing single x values with only one y
testing_ardl10_2 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(0,1,2,3,4,5,6,7,8,9), q=c(1,2,3,4)))
summary(testing_ardl10_2)
##
## Time series regression with "ts" data:
## Start = 11, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07240 -0.01008 -0.00305 0.01205 0.16198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0886163 0.0192214 4.610 2.06e-05 ***
## X.10 0.0028150 0.0006289 4.476 3.32e-05 ***
## Y.5 0.3329674 0.0939228 3.545 0.000753 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03214 on 62 degrees of freedom
## Multiple R-squared: 0.7551, Adjusted R-squared: 0.7472
## F-statistic: 95.6 on 2 and 62 DF, p-value: < 2.2e-16
testing_ardl10_3 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(0,1,2,3,4,5,6,7,8,10), q=c(1,2,3,4)))
summary(testing_ardl10_3)
##
## Time series regression with "ts" data:
## Start = 10, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076992 -0.012253 -0.003747 0.011746 0.155252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0985636 0.0205377 4.799 1.02e-05 ***
## X.9 0.0031174 0.0006673 4.672 1.62e-05 ***
## Y.5 0.3039371 0.0956797 3.177 0.00231 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03163 on 63 degrees of freedom
## Multiple R-squared: 0.7757, Adjusted R-squared: 0.7686
## F-statistic: 108.9 on 2 and 63 DF, p-value: < 2.2e-16
testing_ardl10_4 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(0,1,2,3,4,5,6,7,9,10), q=c(1,2,3,4)))
summary(testing_ardl10_4)
##
## Time series regression with "ts" data:
## Start = 9, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076567 -0.013837 -0.003512 0.007458 0.140990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1221159 0.0219795 5.556 5.73e-07 ***
## X.8 0.0038902 0.0007086 5.490 7.38e-07 ***
## Y.5 0.2491341 0.1004681 2.480 0.0158 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03328 on 64 degrees of freedom
## Multiple R-squared: 0.7843, Adjusted R-squared: 0.7775
## F-statistic: 116.3 on 2 and 64 DF, p-value: < 2.2e-16
testing_ardl10_5 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(0,1,2,3,4,5,6,8,9,10), q=c(1,2,3,4)))
summary(testing_ardl10_5)
##
## Time series regression with "ts" data:
## Start = 8, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.062329 -0.015881 -0.004658 0.011522 0.119788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1400491 0.0204781 6.839 3.34e-09 ***
## X.7 0.0044620 0.0006569 6.793 4.02e-09 ***
## Y.5 0.1769083 0.0931879 1.898 0.0621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03064 on 65 degrees of freedom
## Multiple R-squared: 0.8146, Adjusted R-squared: 0.8089
## F-statistic: 142.8 on 2 and 65 DF, p-value: < 2.2e-16
testing_ardl10_6 = ardlDlm(x = scc_test_big$leaving_home_dif, y = scc_test_big$dlog_cases, p = 10, q = 5, remove = list(p = c(0,1,2,3,4,5,7,8,9,10), q=c(1,2,3,4)))
summary(testing_ardl10_6)
##
## Time series regression with "ts" data:
## Start = 7, End = 75
##
## Call:
## dynlm(formula = as.formula(model.text), data = data, start = 1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.068284 -0.013166 -0.005343 0.007768 0.160932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1090246 0.0212746 5.125 2.80e-06 ***
## X.6 0.0034374 0.0006868 5.005 4.41e-06 ***
## Y.5 0.3178040 0.0909899 3.493 0.000858 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0341 on 66 degrees of freedom
## Multiple R-squared: 0.78, Adjusted R-squared: 0.7733
## F-statistic: 117 on 2 and 66 DF, p-value: < 2.2e-16
Get the zip code case data for SCC - this doesn’t work, running into issues with Rselenium and scrolling
# remDr$navigate("https://app.powerbigov.us/view?r=eyJrIjoiZTg2MTlhMWQtZWE5OC00ZDI3LWE4NjAtMTU3YWYwZDRlOTNmIiwidCI6IjBhYzMyMDJmLWMzZTktNGY1Ni04MzBkLTAxN2QwOWQxNmIzZiJ9&pageName=ReportSectiona1d27339c9acd841e1fa")
#
#
# webElem <- remDr$findElements(using = "class", value = "pivotTableCellWrap")
#
# website_raw <-
# 1:length(webElem) %>%
# map(function(x){
# webElem[[x]]$getElementAttribute("innerText") %>% as.character()
# }) %>%
# unlist() %>%
# as.data.frame() %>%
# mutate(convertedVals = as.character(.))
#
# zips_scc <- zipcode_scc_raw$convertedVals[4:(length(zipcode_scc_raw$convertedVals)/3+2)]
# counts_scc <- zipcode_scc_raw$convertedVals[(length(zipcode_scc_raw$convertedVals)/3+3):(2*length(zipcode_scc_raw$convertedVals)/3+2)]
# perc_pop_scc <- zipcode_scc_raw$convertedVals[(2*length(zipcode_scc_raw$convertedVals)/3+3):length(zipcode_scc_raw$convertedVals)]
#
# zipcode_scc <- data.frame(zips_scc, counts_scc, perc_pop_scc)